Performance Evaluation of Crowd Analysis Algorithm using Modified GMM and Adaptive Thresholding

Abstract

To evaluate the crowd densities in video scene under different constraints. For the crowded video analysis robust foreground detection methods are required to differentiate between moving or static foreground objects and static or dynamic background. Large number of foreground segmentation or motion segmentation approaches are available but only few can handle the various constraints like illumination variations, dynamic background partial or high level of occlusions. Method: We have proposed a modified Gaussian mixture model using adaptive thresholding. The proposed approach is implemented in MATLAB. Findings: Our proposed approach analyze all the aspects of the various backgrounds and foregrounds modelling and then compared their critical performance in terms of the PR curves and miss rate. The performance evaluation demonstrates considerable improvements in miss rate compared to traditional approaches. Our proposed method also shows significant improvements in Multi Object Detection and in Tracking Accuracy. Application: Our proposed approach analyzes the crowded scenes, especially handles outdoor environment. Optimized model parameters and adaptive thresholding makes it more robust to handle varying light conditions and partial occlusions.